, I surely think AI can make a huge difference here. Think about self learning systems for instance. Also regarding "taking emotion out of the machine", AI will have impact. Where often human emotion drives the design or appliance of systems (say driving a car...), AI could help in a positive way... But there are still challenges... Does my car know that the person waving at me is someone who needs help, has bad intentions? Or just ignore the person, as the object is not in the driving path of my car...

I personally think that AI will transform both the process for creating solutions and the structure of the solutions themselves. We've long been stressing that a proper BPM solution must be structured to be easily changed as business requirements change... but we were expecting people to make those changes. A solution that is structured for AI to change is likely quite different... (People benefit from a BPMN diagram... AIs likely don't)

AI and specifically speech recognition will have a huge impact on BPM and on how users interact with processes.Speech IMO will emerge as the dominant user interface for interacting with the web and especially for the IoT. All of the major software/IT organizations now have investments inspeech recognition (Siri, Cortana, Alexa). This is an opportunity for BPM. We’ve discussed here recently about the issues BPMs have always had with the UI, being always just behind the curve. I can see voice becoming a vendor agnostic UI for process. The emergence of chatbots indicates it's already happening.

Integral as data prepared to be used as input or output to/from the AI source. A full audit trail required to ensure reliability of outcomes frankly no different from information created by people? Also essential that when a build of AI capability is undertaken the principles of BPM should be followed to ensure integrity of any AI output.

Yes, expert systems in the 80's -- except "expertise" turned out to be difficult to find AND difficult to formalize -- not to mention the systems weren't up to the tasks. Science fiction is easier to write than to build.

Nothing will happen, because businesses are scared to death of new technology and most certainly no one has any staff to understand any of it. So corporate IT will stay away from it like the Belzebub. That has also been our experience in the last five years even so we do not require knowledge engineers. ML will only happen in the large closed systems that run in the cloud today, like Facebook, Google, Uber and others.

What? Nothing to do with AI. Machine learning identifies patterns in a largely variable environment. BPM enforces business behavior to do away with those variations. What in the world should a machine learning engine find in a BPM system in terms of patterns except a Gauss distribution of data values?

@Max, of course there are valid use cases: an AI would mine process data against established patterns (process models or fragments) and suggest optimization, even real time balancing of resources. That's a suggested practical use in BPM, way before we model chemical emotions in humans.

Like IoT, AI will be one major disruptor around BPM technology. From a use case perspective, AI can be used to generate intelligence and awareness around an existing process and properly identify the conditions and situations that require starting or notifying a business process. The other scenario is in assisting the process participant with a suggested best outcome (for example based on past executions and similar context). Additionally, AI could help prevent bad decisions, by warning the end user of the impact of taking a particular action at a given moment in time in the process.

IMHO, I think AI can help make process better and sooner than later we will see AI integrated with business processes.

"Now I will turn to artificial intelligence, because it occupies a special role in The Fourth Industrial Revolution. In particular, machine learning will turn big data into big workflow. In other words, AI/ML will make big data big actionable, in real-time and at scale.

In many ways, workflow technology, while not a direct descendent of early artificial intelligence research, nonetheless inherits important similar characteristics. Both distinguish between domain knowledge that is acted upon and various kinds of engines that act on, and are driven by, that updated domain knowledge. Workflow engines are like expert systems specializing in workflow. Just as expert systems have reasoning engines, workflow systems have workflow engines [admittedly a very loose analogy, keep in mind I'm not addressing an BPM audience here]. Artificial intelligence and machine learning are critically about knowledge representation. Early AI used logic, current ML uses neural network connection strengths. Modern BPM represents knowledge about workflows, works, plans, goals, activities, resources, and so on.

Finally, many AI systems, especially in the areas of natural language processing and computational linguistics, communicate with human users. When I say “communicate” I don’t just mean data goes in and come out. I mean they communicate in a psychological and cognitive sense. Just as humans use language to achieve goals, so do some AI systems (some using text, but others using visual symbols and gestures). Communication between humans and workflow systems is rudimentary, but real. Workflow systems represent the same kinds of things human leverage during communication: goals, intentions, plans, workflows, tasks, and actions. These representations are, essentially, THE user interface in many workflow systems.

Artificial intelligence and machine learning will play key roles in coordinating intra- and inter-system communication and coordination. So will workflow technology. In fact, workflow models and their execution will increasingly be guided by artificial intelligence logic and machine learning networks. And artificial intelligence logic and machine learning networks will increasingly really on time-stamped workflow event data to create and improve these logics and models.

To sum up, The Fourth Industrial Revolution is not about any one product, technology, or even system. It is about innovation in how multiple systems of technology come together. Artificial intelligence, machine learning, and process-aware technology such as business process management will, together, play a special role in gluing together these systems, so they can be fast, accurate, and flexible, at scale."

Big data produces big rubbish and ML (do not use AI please!!!) compacts that rubbish into a smaller form. It remains rubbish. ML systems do not understand anything ... they just find similarities, but only if the environment is stable and produces a constant, repeatable flow of data. No company has today such a system and such data available. If anyone knows anything about IT than they know that bringing systems together is the biggest problem since reading tapes and is equally the biggest failure of corporate IT. The whole notion of a fourth industrial revolution with AI is simply a puff of smoke.

RE "where can we find use cases where there are big savings" - shall we take into account that in the digital economy, AI has some obvious advantages vs many humans: speed of work, processed volume, precision of operations, ability to learn?

ML systems never understand what they discover, recommend or perform. They simply follow the trotten path. In BPM that path is coded ... what would anyone need ML for? And no, ML is not expensive ... the knowledge engineers are. If you think a BPM workflow is hard to change you have not tried to change the training patterns in an ML system ... or worse if the environment changes so that all previously learned patters no longer apply ... have fun!

I had my first real job (20+ years ago, while still a student) in building an AI system for real-time financial time-series forecasting and tick-by-tick investing. Back then AI was a little more than a black box - an interesting academic reasearch field. The project didn't pan out commercially as it could - turns out big institutional investors in Western Europe wouldn't put their money in black boxes :-)

So I was always fascinated by the field and was also quite acutely aware of its limitations. Some of those fundamental limitations have been overcome in the past 5-6 years, but I think a deep impact on enterprise business is still many many years away.

Yes, it's cool to order your Uber from a bot. But I wonder when will occur the purchase of an aircraft fleet from that same bot.

AI is particularly suited in objective function optimization challenges - business is far from being a field where the objective function is even remotely defined. What is the ultimate objective function of a business? How do you express it in a way that motivates an AI to optimize it?

Interestingly, I think AI is far more suited towards solving mining challenges, therefore can be of much more immediate help in case management scenarios, especially when there's a lot of persisted data to be mined in order to come up with next steps. Medicine (as a natural science) is one clear example, and the most comfortable. Law could be the next one (but since this is an artificial science, therefore with far greater variability, conclusive results may gravitate for a long while around local optima).

So, yes, there's a lot of potential for the technology, but as said in previous posts, I take issue with people blowing the horn too loud, too soon, and throw the promise of this technology into mockery.

Even its most rabid promoters [url=http://www.theverge.com/2016/9/12/12890032/techcrunch-disrupt-david-marcus-bots-facebook-messenger]seem to agree[/url], which is like, good news. Who knows.

Laws, regulations, directives and contracts are actually finite sets of rules. One could imagine a future where such rulesets become more and more part of an open legal standard that can be then easily replicated across business world (and not only).

It's super-difficult, since there's also a lot of cultural gaps that need to be filled and the world is not keen on melting into one cultural identity yet. But it's doable.

This is a bit of a rushed answer. If I have time I'll revisit and expand/tidy up!

First of all we need to unpack "AI". We did a webinar recently that did some of this quite nicely, I think (see replay below), but very briefly there are a number of relevant pieces that any tech strategist needs to think about in this context, and they operate across two distinct layers: interactions and insights.

AI applications at the interaction layer are largely about creating learning systems that enable automations to fit around the natural expectations/envronments of people - rather than expecting people to modify their behaviours to fit around the needs of automated systems.

At the insights layer a lot of this is to do with advanced pattern detection, recommendation and advisory capabilities.

I see AI applications in the modern workplace as delivering value in two main types of scenario: firstly, in increasing the impact of scarce expert resources in the context of high-expertise tasks (think cancer diagnosis). Secondly, in automating more aspects of procedural high-volume tasks.

How will these things impact BPM?

If we're looking at BPM as a set of technologies to enable work co-ordination at scale then initially, the impact will be at the edges - it will shape new user interaction options around task management and execution, for example. Utimately there's room for AI applications to directly augment process, task selection and assignment.

If we define an "expert resource" as merely a human that has been exposed to (and ingested, and memorized) a lot of casework and is able to reliably retrieve relevant historical experience to current casework, we see how actually AI helps in medicine for example. This is actually happening already - large labs do not rely on teams of histopathologists anymore, it's computers doing pattern recognition on huge databases of microscopic tissue imagery and coming up with suggested diagnostics. A histopathologist may still supervise the pattern recognition algorithm and may still vet the final results, but her/his productivity is now hundreds of time higher.

The questions and many answers show a lack of understanding what technology can do today or might do in the future. Even the coolest self-driving cars are today dangerous pieces of automated plastic unless all human and natural interaction with the road system is removed. It can't distinguish between a rabbit and a baby on the road and can't make a human judgement of such situations. Drones can fly highly autonomously but they are small so errors just end it a little heap of junk. AI isn't happening yet or soon.

As you might know my stance towards BPM is that it is utterly useless in improving what a business does on a human level. It can however automate and dumb down business interactions so they no longer require much intelligence or knowledge ... which has been so expensively analyzed through methodology and practice. Therefore it is odd to then consider that BPM will be improved, amended or replaced by AI, which everyone should really be calling machine learning or ML. To do anything sensible by itself AI would have to emulate human emotions to simulate human intelligence and as that is a chemical experience engine it most likely never will in pure software. Also self-awareness will not happen as we know nothing about the functionality of the medulla -- sitting between the spinal cord and the brain -- which enables that.

But .... ML is a perfect replacement for the orthodox, and rather shortsighted concept of BPM because it will not require any kind of analysis, or monitoring or improvement as all that could happen through machine learning. But learning how and from whom and with what accuracy? We have taken that step to use ML for automated process discovery and Next Best Action recommendations about 5 years ago (the patent is a few years older) and found that no one had any interest in using it, mostly for odd reasons. This included general fear of the technology and possible errors, and aloof rejection of the idea that software could actually do that. Well, it does work in our platform as the famous User-Trained Agent, but those who do BPM do not get it or do not want it ... much like Adaptive Processes. Those who do not like BPM also do not like any ML functionality connected to it and prefer hard-coded applications that after rigorous testing produce a frozen and dead version of business knowledge that becomes instant legacy dead weight to the business.

We do not use AI to emulate human reasoning (which is purely emotional and the reason it works so well) but simply observe human actions and interactions in a well-defined environment of our platform and once the ML software sees repeated patterns of actions and data it will start to recommend these actions, no longer requiring all the BPM mumbo jumbo. But still, there is little interest given the hords of BPM experts who need a job.

A key problem of human interaction -- may it be in written form or speech -- is ambiguity. Humans solve it through context which computers find really hard. Modern speech recognition only works so well as it uses a dictionary and grammar library to turn gibberish into correct words and sentence structures, which we did 15 years ago for OCR recognition. For a business interaction more is required, as much as I agree that speech is the computer interface of the future. A grammatically correct sentence can still not make any business sense at all and we do not gain great benefits if our inputs are single word answers to questions.

Which is why we focus on building ontologies that help to clearly define the terminology of a knowledge domain. I wish we would be able to ML that part but that will still take a little while. But once user input can be made matching to a domain knowledge model, ambiguity in design and Use Case Interactions is reduced and simple text or speech becomes well-working input to an application. ML can learn what inputs it recognizes correctly in a given context of a business architectured capability map and interface the user to the right transactions, guided by user-defined boundary rules and regulative constraints. And yes, that is all part of the patent.

It is not yet simple enough, but that approach will be a practical use of ML for improving the transactional collaboration that every business performs to fulfill its goals and serve its purpose ... also defined through same domain terminology. It does make BPM utterly obsolete. Try to link that with a typical approach to BPM and I truly fail to see why one would bother with the overhead. Unless ... the definition of BPM is simply once more expanded to include not only the heave-ho IoT but also all forms of AI. People surely seem to try ...

ML is surely just one example of "intelligence" that can be used in business? As for BPM is dead you have got wrong end of stick there....it is only beginning as supporting software capabilities expand where there will be no limitations to user thinking being delivered and creating Adaptive applications covering all aspects of the operational needs of business.

So fun to beat around the bush on this subject :-) Some quite outlandish scenarios... I'm surprised the famous tweeting fridge hasn't made a cameo yet.

But frankly, if you take a look at the successful applications of AI today, they are almost exclusively centered around

[b]pattern recognition[/b]

, which for business just translates into faster, more convenient ways of providing human input (text, data, images, videos) to systems, or just faster ways to query otherwise humanly impossible combinations...

I also read that AI will generate intelligence and ML will happen without any kind of analysis, corection, supervision...

Hard to believe that - there'd rather be a lot of humans working for the

yes, but those who tell the machines what to do are likely to find themselves on the wrong end of that conversation. they often can't look far enough into the future to see it coming.
Example: a colleague of mine was sent to another country to train people that cost 1/4 or less what the staff in the USA cost. What he trained them in was highly proprietary, difficult to learn, and no publicly available information for someone to learn from.

Six months later he came home, and was upset to find out his 3 best friends from work had been laid off. He had trained their replacements himself, and put them out of work. Then they laid him off a few months later. Then, the whole team in that other country turned over within 12 months (100% turnover). All the knowledge was lost to the company. He didn't have to go do that training, but he thought "if not me, then someone else will." Very healthy logic, that.

So... he never looked far enough ahead to see that being the guy telling someone else what to do may not be a stable situation.

Silver lining - that company now pays $250/hour++ for people to do this work, because the only people who know how to do it are in their 40's-50's and live in the USA. And the need for it is still there, though the demand is down considerably as the systems are gradually replaced. Their attempt at cost savings was one of the most expensive mistakes they could have made. Not sure what the moral of the story is, but it is a cautionary tale :)

Peter -- can BPM.com maybe support "likes" on replies? @Scott's story about the loss of deep domain knowledge is fantastic -- the intersection of technology, economics, governance -- and the roller coaster of personal life in modern business.

@Scott I wish that story wasn't so common place. The end is always the same too. I still don't understand how C level execs think that knowledge workers are like cogs in a machine that you can simply replace.

When you start to get to the upper levels of domain knowledge you find that people bring their own styles. These devs/consultants/managers/etc might as well be artists. You can't just tell someone how you paint and expect them to paint like you after a few months of training.

I do take solace in the fact that it always comes back around to needing the knowledge worker. To your point, it's usually more expensive once it becomes a service. But don't worry C levels, this next time it will work! (that's sarcasm)

Right now, it boils down to not having a clear answer on how to keep costs down. As multiple posts have already called out, this is bleeding edge stuff. That's why you see the big players in the industry sinking tons of R&D into machine learning, natural language processing, and voice recognition.

As a developer, I look at ways we can pick up parts of this tech stack to help out the implementors of process. Personally, I'm always interested in better ways to get a customer started on their process. Being able to design an AI solution that can read existing data and discover possible process flows would be a big improvement.

Part of the issue on getting this going is normalizing the data that would be put into such a system. The real customer value would be to have the ability to just dump everything they have into the system and the system would be smart enough to normalize it. This isn't impossible, it's just really expensive to get going.

I think about "Back to the Future" when Doc Brown shows back up at the end and just starts throwing garbage into the Delorion. He didn't care what it was, it was fuel for the time machine. Can't we take that same approach with data and AI? Wouldn't a system that you could throw all your data into and be given a process qualify as a time machine? It took you forward in time to a point where you had the process. 88 mph Marty!

I think AI is a misnomer for this conversation. It leads us down the rathole of discussing this in terms of replacing human endeavor with that of a machine. The point of all this artificial intelligence, or machine learning, or frankly, just automation - is to *assist* and *augment* the human endeavor.

Example: Self Driving cars.

If the endeavor is to drive, self-driving cars are inappropriate. But all the assist technology - automatic braking, even ABS, cruise control, stability / traction controls, etc. are all in the name of augmenting the human driver. All good stuff.

If the endeavor is to transport oneself from point A to point B, self-driving cars make sense. They'll augment that transportation experience, potentially allowing you to read a book like you would on the metro, and yet start and stop at completely custom lcoations.

But often we talk about AI in such a way that there's no person to be transported from A to B. The car will know where to go on its own, based on pattern matching, and will do everything itself. Except, without a human to transport, what is the purpose of the car?

What purpose BPM without the people? What purpose business, without people? Human endeavor is what lends all of these efforts purpose and why we should always be thinking about augmentation more than replacement, in my humble opinion.

I like the term "force multiplication" -- and we can divide that up according force application for physical effort or cognition. A whole world of literature and research and experience devolves on technology and tools.

But for BPM and process automation a good argument can be made that the "for what" is always about "decisioning".

It's odd to see lots of work on "big data" and "analytics" or in our case, process automation, and to note that often the centrality of decisioning is lost.

Management concerns the making of decisions, and everything that goes into making decisions. Staff work and research doesn't matter at all until the leader or officer makes a decision. Or the electorate makes a decision.

AI is a case in point; the only purpose of AI is to help humans make decisions.

If one accepts this, and if this observation is more than just a trivial observation, then one begins to look at AI and BPM a little differently.

AI-for-itself is as useless as a process-for-itself. We use technology to help us do more work. And work consists of physical effort and decisioning. So how is technology helping us do more?

Walter -- your question is a good one, for sure! And I deliberately left out "decisions for agents, or machines" and any question of the "definition of work" and "how decisioning is part of work", otherwise the comment would be too long (!).

But consider use cases (and business case justifications too) -- any "decisions" made by AI are always made for the benefit of human beings, even if indirectly. One could say that this is a sophistry, but I argue that a decision is a decision is a decision (just like Gertrude Stein said).

Consider a spectrum of assisted decision making, from spreadsheets, through BI and BAM, through credit scoring and decision tables, through "autonomous agents with rules". Sometimes humans remain in the loop but the end-case is already today that some lower-level decisions are entirely by machine. (Note that there are multiple actors involved in software-assisted decisioning, both as deciders and as immediate and indirect beneficiaries.)

Until the Singularity, all decisions are still at some level by and for humans.

A focus on decisioning is important because otherwise, why bother? Who cares about AI or about cognitive computing if they are not in some way helping us decide something? What else is there to do?

I think I will steer clear of describing decision support or automated decisioning as "AI".

There is not a long list of "magic" in the ACM/BPM endeavors our group gets involved in (e.g.auto-branching decision boxes, process control points along BPM pathways, dropout counters on loopbacks). For me, these are just rule sets.

Our medical Case Management product has a sophisticated in-line diagnostic algorithm that is able to highlight candidate diseases on the basis of symptoms/signs. We don't refer to it as AI.

We developed this in the early 1990's for use in Toxicology and enjoyed touring for a number of years where we would go into a doctor's office, hand them a copy of Robert Driesbach's "Handbook of Poisoning", ask them to open any page, have them read us 3 symptoms/signs and the algorithm would tell them what page they were on..

We stopped distributing the symptom/sign to disease cross tables as the cost of insurance was too high.

During the years I lived on airplanes, I discovered, quite by accident, a way to avoid having to chat with people sitting on either side of me.

This one time I was reading Handbook of Poisoning and a lady beside me remarked that this was "unusual reading".

My response was "I have been having problems with my mother in law" and this caused the lady to flatten herself against the window for the remainder of the flight.

I agree absolutely with 'A focus on decisioning is important" - in healthcare this is the ultimate value of Case Management aside from long term outcomes data collection in that no intervention should be attempted without consulting the e-chart (Case History, EHR, EMR etc).

The docs need to be able to view the e-chart in reverse chronological order to see what has been done.

They also need, at times, to see a marked-up BPM diagram to see what has NOT been done (i.e why, last August, did John Doe not receive an X-Ray when the step was a step in the best practice protocol?)

We are a long away from the time when e-chart reviews get replaced by AI.

What's completely transformative today which was never the case in the past is that 3 BILLION people already chat apps daily. Yes, read that again.

This means that people are now not just chatting on chat apps, they actually doing WORK on chat apps. This means that that UI is now gone - people expect to do process steps in a chat app they already use.

Here's the key thing - because the current BPM industry is so focused on automation and modelling using flowcharts - this is inaccessible to the average user. Hence, we are trying a simpler approach of a checklist-like UI - it's proving to be wildly successful.

By doing this, the next piece of rationale dictates that people are now EXECUTING and doing steps within our UI and eventually - our chat bot. This solves the biggest missing pieces of BPM today for businesses:

[list="1"] [*]
Making it ridiculously easy for an average person to write up a process without using flowcharts. [*]
Making it crazy easy to do the process itself within a beautiful web UI or within a chat app they already use. [/list]

That's where ML comes in. Bayesian style learning can easily record an outcome e.g. a rating or the fact that a process or step was late. Given enough outcomes, it can predict process failure BEFORE it happens - since we are actually recording steps people are doing. The data is gold dust and does not exist today.

Then we have the NLP portion of AI. The aforementioned chat bot will need to understand discrete actions a user takes. Normally these are really simple e.g. just answer yes/no - but the nuances come in when there's more choices. Given that BPM is not about asking open ended questions (like Google search) - BPM and workflow is by far the most easy to merge into a chat bot.

Ultimately, what you can do in a chat bot can also be done with voice - e.g. Amazon Echo. That's just a layer on top of chat.

Hope that adds light to all this darkness. We're actually building the above.

Amit, very cool to highlight the amazing opportunities (which involve NLP and workflow and BPM) by huge numbers of people outside the world of IT!

As for any definition of AI, the term is such an umbrella term for many things. I've highlighted decisioning as centrally important. Certainly lots of people include NLP under the umbrella of AI. Consider though the idea of language itself -- which is a very difficult computing problem. Whereas original research on language was focused on semantics, language processing now is apparently mostly statistical.

As humans we all produce language. But however difficult the challenge, speech is still just a channel. The perceiving, thinking -- and deciding -- that goes on in the head is the key.

Here's an terrific WSJ article by Thomas H. Davenport in 2014 on the various meanings of AI:

Honestly, I feel that AI is another buzzword. Not in the sense that it's pointless or useless but in the sense that it's just a new word to indicate the same thing. My point is you can read this thread and substitute the word AI for "technology" and it will still work.

Tech is here to make our lives easier, take away the tasks we don't want to do, and let us focus on the growth of our bsuiness.

In this sense AI is not an Oracle like (pun intended) predictor of the future of the industry, With or w/out AI, BPM tech is [url=http://connecteam.com/mobile-workforce-management-app-management-just-got-easy/]about automating processes[/url]. The future will allow SMBs to do so with the same efficiency that enterprises do it and across devices.

In my view, the convergence of AI and BPM shows increasing relevance of BPM and supporting technologies in the modern enterprise. It’s a natural continuation of rules driven optimization to intelligent, adaptive and now, cognitive BPM. The era of smart business processes touted by self-proclaimed visionaries a few years ago could be just around the corner.

The increase of marketing and investment activity by consulting companies, BPMS vendors, and large service providers would indicate that many market leaders are trying to define their destiny right now and move in this direction, trying to realize the first mover advantage.

The AI dimension raises the bar for many pure-play BPMS vendors. For example, how do you differentiate iBPMS from AI driven BPMS? How do you assess these new and still emerging capabilities? Do they belong as a core capability of intelligent BPMS/Low Code platforms or should we interface with them IBM Watson’s style, through dialog services? Most likely, market adoption driven by tech leaders and early adopters will help answer these questions. Microsoft, Google, and perhaps Facebook, will try to democratize AI, make it common, low cost and available in many products we use every day. Thus, AI seems like a much broader trend that will impact many areas of the enterprise, not just BPM.

Designing architecture of enterprise BPM systems reminds me of the problem of managing traffic in the growing city of Seattle. Seattle’s economy is vibrant and creates many jobs, attracting new workers to the city. The city’s road infrastructure is aging and connecting quickly growing metro with new arteries means either destroying old roads, costly investments into new roads or multiyear investments into railroads due to complete in 15-20 years.

Would the introduction of AI (in this case self-driving cars) help to address this painful problem? The Autonomous Vehicle Plan for the Seattle/Vancouver B.C. Corridor by Madrona Venture Capital promises significant traffic reductions by introduction of autonomous vehicle lanes: http://www.madrona.com/i-5/

Going back to AI and BPM, it’s easy to envision parts of the enterprise completely driven by AI engines for certain types of activities. By augmenting human work and solving temporary but super painful workload congestion problems, not fully replacing us. Once thing for sure, such future will not emerge in the EU, which has already attempted to highly regulate applications of AI systems. Early adopters always pay the price but in the end, it may be worth it.

BJ - do you believe this is also about a movement of BPM away from IT-led or expert-led implementation to self-service and business user adoption? If so, that's where the disruption is.

BPMN is far too complicated and does not represent how modern work gets done. This is not just about more automation - it's also about consumerization and broadening access to process tracking. IMO - collaborative workflow (how people actually do work) combined with OS-supported NLP (for chat and voice) and machine learning (for predicting process failures) is the future.

Today, one of the biggest challenges BPM is facing is its ability to adapt to fast business changes. Flexible BPM engine and/or the addition of DMN and CMMN are already trying to provide solutions to this challenge.

Recent progress in AI technology, more specifically progress in ML, will accelerate this transformation as it will easily identify the bottlenecks in complex workflow and suggest improvements.

As a consequence, it will require the BPM solutions provider to adapt their solutions in order to take in account this new inputs and transform workflows accordingly. Real-time trading solutions have been applying this model with success for years now.

A very good question! There indeed seems to be a delta when it comes to technical advances, tendencies and, ultimately, real-life benefits from process optimization and automation. For the extended f...